Volume 10 Issue 1 | Journal of Medical Imaging

Journal of Medical Imaging

VOL. 10 · NO. 1 | January 2023

CONTENTS

IN THIS ISSUE

Editorial (1)

Image Processing (7)

Image-Guided Procedures, Robotic Interventions, and Modeling (1)

Image Perception, Observer Performance, and Technology Assessment (2)

Biomedical Applications in Molecular, Structural, and Functional Imaging (2)

Digital Pathology (2)

Errata (1)

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Editorial

2022 List of Reviewers

Journal of Medical Imaging, Vol. 10, Issue 01, 010102, (January 2023) https://doi.org/10.1117/1.JMI.10.1.010102

TOPICS: Lithium, Chaos, Mendelevium, Germanium, Alternate lighting of surfaces

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Image Processing

Identification of infarct core and ischemic penumbra using computed tomography perfusion and deep learning

Mohammad Mahdi Shiraz Bhurwani, Timothe Boutelier, Adam Davis, Guillaume Gautier, Dennis Swetz, Ryan Rava, Dorian Raguenes, Muhammad Waqas, Kenneth Snyder, Adnan Siddiqui, Ciprian Ionita

Journal of Medical Imaging, Vol. 10, Issue 01, 014001, (January 2023) https://doi.org/10.1117/1.JMI.10.1.014001

TOPICS: Shadows, Tunable filters, Voxels, Computed tomography, Image segmentation, Tissues, Wavelets, Diffusion weighted imaging, Optical spheres, Education and training

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Improved bias and reproducibility of coronary artery calcification features using deconvolution

Yingnan Song, Ammar Hoori, Hao Wu, Mani Vembar, Sadeer Al-Kindi, Leslie Ciancibello, James G. Terry, David Jacobs Jr, John Carr, David Wilson

Journal of Medical Imaging, Vol. 10, Issue 01, 014002, (January 2023) https://doi.org/10.1117/1.JMI.10.1.014002

TOPICS: Blind deconvolution, Reproducibility, Calcium, Point spread functions, Image deconvolution, Heart, Computed tomography, Arteries, Deconvolution, Calibration

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Purpose

Our long-range goal is to improve whole-heart CT calcium scores by extracting quantitative features from individual calcifications. Here, we perform deconvolution to improve bias/reproducibility of small calcification assessments, which can be degraded at the normal CT calcium score image resolution.

Approach

We analyzed features of individual calcifications on repeated standard (2.5 mm) and thin (1.25 mm) slice scans from QRM-Cardio phantom, cadaver hearts, and CARDIA study participants. Preprocessing to improve the resolution involved of Lucy–Richardson deconvolution with a measured point spread function (PSF) or three-dimensional blind deconvolution in which the PSF was iteratively optimized on high detail structures such as calcifications in images.

Results

Using QRM with inserts having known mg-calcium, we determined that both blind and conventional deconvolution improved mass measurements nearly equally well on standard images. Further, deconvolved thin images gave an excellent recovery of actual mass scores, suggesting that such processing could be our gold standard. For CARDIA images, blind deconvolution greatly improved results on standard slices. Bias across 33 calcifications (without, with deconvolution) was (23%, 9%), (18%, 1%), and (−19 % , −1 % ) for Agatston, volume, and mass scores, respectively. Reproducibility was (0.13, 0.10), (0.12, 0.08), and (0.11, 0.06), respectively. Mass scores were more reproducible than Agatston scores or volume scores. For many other calcification features, blind deconvolution improved reproducibility in 21 out of 24 features. Cadaver images showed similar improvements in bias/reproducibility and slightly better results with a measured PSF.

Conclusions

Deconvolution improves bias and reproducibility of multiple features extracted from individual calcifications in CT calcium score exams. Blind deconvolution is useful for improving feature assessments of coronary calcification in archived datasets.

Multislice input for 2D and 3D residual convolutional neural network noise reduction in CT

Zhongxing Zhou, Nathan R. Huber, Akitoshi Inoue, Cynthia McCollough, Lifeng Yu

Journal of Medical Imaging, Vol. 10, Issue 01, 014003, (January 2023) https://doi.org/10.1117/1.JMI.10.1.014003

TOPICS: 3D modeling, Performance modeling, Denoising, Computed tomography, Education and training, 3D image processing, Veins, Infrared imaging, Image quality, Convolutional neural networks

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Unsupervised convolutional autoencoders for 4D transperineal ultrasound classification

Frieda Van den Noort, Claudia Manzini, Merijn Hofsteenge, Beril Sirmacek, Carl Van der Vaart, Cornelis H. Slump

Journal of Medical Imaging, Vol. 10, Issue 01, 014004, (February 2023) https://doi.org/10.1117/1.JMI.10.1.014004

TOPICS: Education and training, Ultrasonography, Muscles, Principal component analysis, Statistical analysis, Motion analysis, Data modeling, Diseases and disorders, Design and modelling, Visualization

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CaraNet: context axial reverse attention network for segmentation of small medical objects

Ange Lou, Shuyue Guan, Murray Loew

Journal of Medical Imaging, Vol. 10, Issue 01, 014005, (February 2023) https://doi.org/10.1117/1.JMI.10.1.014005

TOPICS: Image segmentation, Polyps, Medical imaging, Tumors, Brain, Data modeling, Performance modeling, Convolution, Surgery, Neuroimaging

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Retinal layer and fluid segmentation in optical coherence tomography images using a hierarchical framework

Tânia Melo, Ângela Carneiro, Aurélio Campilho, Ana Maria Mendonça

Journal of Medical Imaging, Vol. 10, Issue 01, 014006, (February 2023) https://doi.org/10.1117/1.JMI.10.1.014006

TOPICS: Image segmentation, Education and training, Optical coherence tomography, Retina, Matrices, Lithium, Deep learning, Retinal diseases, Performance modeling, Cross validation

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Automatic landmark correspondence detection in medical images with an application to deformable image registration

Monika Grewal, Jan Wiersma, Henrike Westerveld, Peter A. Bosman, Tanja Alderliesten

Journal of Medical Imaging, Vol. 10, Issue 01, 014007, (February 2023) https://doi.org/10.1117/1.JMI.10.1.014007

TOPICS: Deformation, Computed tomography, Medical imaging, Image registration, Education and training, Biomedical applications, Magnetic resonance imaging, 3D image processing, Anatomy, Voxels

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Image-Guided Procedures, Robotic Interventions, and Modeling

Decoding the radiomic features of dorsolateral prefrontal cortex in individuals with accelerated cortical changes: implications for personalized transcranial magnetic stimulation

Hanna Lu, Jing Li, Sandra Sau Man Chan, Winny Wing Yin Yue, Chiu Wa Lam

Journal of Medical Imaging, Vol. 10, Issue 01, 015001, (January 2023) https://doi.org/10.1117/1.JMI.10.1.015001

TOPICS: Radiomics, Single crystal X-ray diffraction, Magnetic resonance imaging, Brain, Prefrontal cortex, Head, Cognitive modeling, Neuroimaging, Brain stimulation, Electric fields

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Image Perception, Observer Performance, and Technology Assessment

Visual expertise is more than meets the eye: an examination of holistic visual processing in radiologists and architects

Spencer Ivy, Taren Rohovit, Jeanine Stefanucci, Dustin Stokes, Megan Mills, Trafton Drew

Journal of Medical Imaging, Vol. 10, Issue 01, 015501, (January 2023) https://doi.org/10.1117/1.JMI.10.1.015501

TOPICS: Visualization, Eye, Radiography, Eye tracking, Information visualization, Radiology, Education and training, Windows, Medical imaging, Design and modelling

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Modeling human observer detection in undersampled magnetic resonance imaging reconstruction with total variation and wavelet sparsity regularization

Alexandra O’Neill, Emely Valdez, Sajan Lingala, Angel Pineda

Journal of Medical Imaging, Vol. 10, Issue 01, 015502, (February 2023) https://doi.org/10.1117/1.JMI.10.1.015502

TOPICS: Wavelets, Image restoration, Magnetic resonance imaging, Image quality, Signal detection, Modeling, Performance modeling, Data modeling, Signal to noise ratio, Medical image reconstruction

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Purpose

Task-based assessment of image quality in undersampled magnetic resonance imaging provides a way of evaluating the impact of regularization on task performance. In this work, we evaluated the effect of total variation (TV) and wavelet regularization on human detection of signals with a varying background and validated a model observer in predicting human performance.

Approach

Human observer studies used two-alternative forced choice (2-AFC) trials with a small signal known exactly task but with varying backgrounds for fluid-attenuated inversion recovery images reconstructed from undersampled multi-coil data. We used a 3.48 undersampling factor with TV and a wavelet sparsity constraints. The sparse difference-of-Gaussians (S-DOG) observer with internal noise was used to model human observer detection. The internal noise for the S-DOG was chosen to match the average percent correct (PC) in 2-AFC studies for four observers using no regularization. That S-DOG model was used to predict the PC of human observers for a range of regularization parameters.

Results

We observed a trend that the human observer detection performance remained fairly constant for a broad range of values in the regularization parameter before decreasing at large values. A similar result was found for the normalized ensemble root mean squared error. Without changing the internal noise, the model observer tracked the performance of the human observers as the regularization was increased but overestimated the PC for large amounts of regularization for TV and wavelet sparsity, as well as the combination of both parameters.

Conclusions

For the task we studied, the S-DOG observer was able to reasonably predict human performance with both TV and wavelet sparsity regularizers over a broad range of regularization parameters. We observed a trend that task performance remained fairly constant for a range of regularization parameters before decreasing for large amounts of regularization.

Biomedical Applications in Molecular, Structural, and Functional Imaging

Measurement of enhanced vasa vasorum density in a porcine carotid model using photon counting detector CT

Jeffrey F. Marsh Jr., Andrew J. Vercnocke, Kishore Rajendran, Shengzhen Tao, Jill L. Anderson, Erik L. Ritman, Shuai Leng, Cynthia H. McCollough

Journal of Medical Imaging, Vol. 10, Issue 01, 016001, (February 2023) https://doi.org/10.1117/1.JMI.10.1.016001

TOPICS: Arteries, Iodine, Animals, Computed tomography, X-ray computed tomography, Image enhancement, Tissues, Attenuation, Animal model studies, Voxels

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In vivo measurements of lung function using respiratory-gated micro-computed tomography in a smoke-exposure model of chronic obstructive pulmonary disease

Nancy L. Ford, Ian Lee, Julia Hwangbo, Anthony Tam, Don D. Sin

Journal of Medical Imaging, Vol. 10, Issue 01, 016002, (February 2023) https://doi.org/10.1117/1.JMI.10.1.016002

TOPICS: Lung, In vivo imaging, Computed tomography, Chronic obstructive pulmonary disease, Tomography, Emphysema, Animals, Animal model studies, Tissues, Histograms

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Digital Pathology

Single patch super-resolution of histopathology whole slide images: a comparative study

Mehdi Afshari, Saba Yasir, Gary Keeney, Rafael Jimenez, Joaquin Garcia, Hamid Tizhoosh

Journal of Medical Imaging, Vol. 10, Issue 01, 017501, (January 2023) https://doi.org/10.1117/1.JMI.10.1.017501

TOPICS: Histopathology, Lawrencium, Education and training, Super resolution, Diagnostics, Pathology, Deep learning, Network architectures, Image resolution, Tissues

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Using deep learning to predict tumor mutational burden from scans of H&E-stained multicenter slides of lung squamous cell carcinoma

Salma Dammak, Matthew J. Cecchini, Daniel Breadner, Aaron D. Ward

Journal of Medical Imaging, Vol. 10, Issue 01, 017502, (February 2023) https://doi.org/10.1117/1.JMI.10.1.017502

TOPICS: Education and training, Cancer, Tissues, Tumors, Lung, Performance modeling, Deep learning, Tumor growth modeling, Lung cancer, Biopsy

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Errata

Errata: Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction

Sahil Sunil Nalawade, Fang Yu, Chandan Bangalore Yogananda, Gowtham Murugesan, Bhavya Shah, Marco Pinho, Benjamin Wagner, Yin Xi, Bruce Mickey, Toral Patel, Baowei Fei, Ananth Madhuranthakam, Joseph Maldjian

Journal of Medical Imaging, Vol. 10, Issue 01, 019801, (January 2023) https://doi.org/10.1117/1.JMI.10.1.019801

TOPICS: Chromium, Image classification, Motion models, Cross validation, Neodymium, Tumors, Brain, Deep learning, Neuroimaging, Magnetic resonance imaging

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